查询词典 data mining

This paper summarizes the background and advantage of data mining, as well as the significance of data mining in the scientific research management of colleges and universities firstly, and then discusses the theory of data mining, association rules and the ideas of main algorithm, analyzes the classic Apriori algorithm and its existing problems as well as the basic solutions. After that, this paper proposes Multi-Dimensional Apriori algorithm which is designed specially for the mining of this paper; Then describes the structure of scientific research data mining system, defines subject-oriented mining tasks, including: mining the data about research projects, mining the data about papers, mining the data about academic writings. The association mining process is implemented by programing, a number of stimulating association rules are found, interpreted and analyzed.

本文首先综述了数据挖掘的研究背景、意义以及数据挖掘技术在高校科研管理中的应用现状和意义，然后在对数据挖掘相关理论、关联规则思想及主要算法进行讨论，分析经典Apriori算法及其存在的问题、基本解决方案后，提出了适合本文挖掘的多维Apriori算法的设计方案，并应用于本文挖掘中；接着论文介绍了科研数据的关联挖掘系统的结构，确定了面向主题的挖掘任务，包括：科研项目信息的挖掘、论文信息的挖掘、学术专著信息的挖掘等；设计了关联规则的实施过程，并通过程序编码得以实现，获得了多条有启发性的关联规则，并对其进行了解释与分析。

Object of data mining is service concepts of service bag, carrier of data mining is history data, and spirit of data mining is mining classification.

在对服务概念开发进行数据挖掘时，服务包中的服务概念是数据挖掘的对象，历史数据是数据挖掘的载体，分类挖掘是数据挖掘的指导思想。

Work in this paper focuses on the data mining from chromatographic retention index data. A retention index database that contains about 50000 records of retention index is firstly established. Projection pursuit technique is then utilized to do data mining upon the data in order to find out some valuable information about the relationship between the retention indices and structural descriptors. A novel algorithm for projection pursuit is developed in this work. Samples of alkane, alkene and cycloalkane are investigated.

中文题名化学数据挖掘新算法和定量构性关系基础研究副题名外文题名 New methodology in chemical data mining and foundational research on QSPR 论文作者杜一平导师梁逸曾学科专业分析化学研究领域\研究方向化学计量学学位级别博士学位授予单位湖南大学学位授予日期2002 论文页码总数115页关键词定量构性关系化学计量学数据挖掘馆藏号BSLW /2003 /O6 /2 化学数据挖掘正逐渐引起化学家们的关注。

And the efficiency and precision of three methods have been analyzed and contrasted by using the CRISP_DM (Cross-industry Process for Data Mining) frame and the step of business understanding, data understanding, data preparation, modeling evaluation and development.

以CRISP_DM(Cross-industry Process for Data Mining)方法论为建模过程框架，按照商业理解、数据准备、建立模型、模型评估、模型发布的步骤，在建模过程中对三种算法的效率和精度进行分析和对比。

From October 16th to 17th, 2006, lead by the subdecanal Hong Xiaowen, the five visitors from Microsoft Research Asia came to visit Nanjing University. On the afternoon of 16th, Doctor Ma Weiying's lecture "Face Recognition and Computer Vision Research at Microsoft Research Asia" was held in room 109 of the technology building in Nanjing University. The room was full of people at that point. On the afternoon of 17th, Doctor Ma Weiying's lecture "Web-scale Data Mining for Search" was held. At the same time, hosted by Gong Yongen, the schoolfellow of Nanjing University and software development engineer in searching technology center of MRA, the experience sharing meeting was held. They won the same nice echo.

2006年10月16日至17日，以洪小文副院长为首的微软亚洲研究院访问团一行5人访问南京大学。10月16日下午，汤晓鸥博士学术报告《Face Recognition and Computer Vision Research at Microsoft Research Asia》在南京大学科技楼109报告厅进行，现场座无虚席。10月17日下午，马维英博士学术讲座《Web-scale Data Mining for Search》以及南京大学校友、微软亚洲研究院搜索技术中心软件开发工程师龚永恩主持的校友经验分享会同时进行，同样取得了热烈的反响。

Eveloped by the Data Mining Group , an independent, vendor led committee, PMML provides an open standard for representing data mining models.

ata Mining Group是一个基金支持的独立的组织，其开发的PMML语言是一种开放的数据模型描述语言。

This paper is based on data mining technology,use weka as a library data mining software tool,use weka's j48 tree algorithm and data association analysis library data.mining useful data which user needs from the mass library data and get reasonable results.ultimately to improve work efficiency and scientific management.

该文以数据挖掘技术为基础，利用weka软件作为图书馆数据挖掘工具，通过weka里的j48树算法和数据关联等算法，对图书馆的馆藏数据进行相应的分析，从海量数据中挖掘出用户需要的有用数据，并得到合理的统计结果。最终达到提高工作效率，能够科学管理的目的。

The major achievement of this paper is: Based on characteristics of the traffic data distribution, execute pattern recognition operations on traffic condition on two dimensions by clustering, then use BP neural network to describe and forecast traffic flow aiming at each pattern. Making use of classic flow-occupancy inverse "V" model, implement polynomial fitting using least-squares algorithm and statistics method on flow curves to detect outliers which are proved to be not accord with practice through the actual implement, then use the moving average model to recorrect the outliers and absent. Make correlation analysis on muti-direction flow queues of the intersection and ones of upriver intersections, choose flow queue with high correlation as assistant one to improve the error tolerance of the prediction system, at the same time we can use the method to give an estimation of flow in intersection with out sensors. We design and implement an SOA(Service-Oriented Architecture)-based UTDD(urban traffic data mining development) with high expansibility and performance, which implement unified management and call of the data-mining application though defining a XML-based description of data-mining process and a common interface to call data-mining process, finally we build traffic flow prediction application model on UTDD.

根据交通流量数据分布的特征，提出基于k-means的二次聚类方法，对交通流量在流量大小和时间上进行模式划分，进而对各个交通流模式进行基于BP神经网络的描述和预测，从而提高模型对流量预测的精度； 2）根据流量/时间占有率倒&V&字形曲线分布模型，提出基于最小二乘法的三次多项式曲线拟合和统计方法的异常检测方法，实际应用表明该方法能够有效识别异常数据，然后根据移动平均算法对异常数据进行修正； 3）基于序列相关性分析，分别对预测方向的交通流量数据序列、上游路口相关序列以及预测路口其它各个方向上的交通流量序列进行分析，选择相似性流量序列，作为辅助序列提供其他没有检测器路口的流量估计； 4）设计和实现了基于SOA（Service-Oriented Achitecture）的高性能、可扩展的智能交通数据挖掘系统UTDD，该系统通过定义基于XML的数据挖掘过程描述和通用的过程模型接口，实现数据挖掘应用的统一管理和调用，最后在UTDD上建立了基于路口流量预测的应用模型。

In a database the concept of an example might change along with time which is known as concept drift When the concept drift the classification model built by use of old data is unsuitable for classify new data Therefore concept drift has become a hot issue in data mining in recent years Although many algorithms had been proposed to resolve this problem they can not provide users with the reason of concept drift However a user might be very interested in such rules For example doctors want to find what makes disease change; researchers want to know the reason of the variety of the weather; and decision makers would like to understand why a customer's shopping habit change In this thesis we propose a Concept Drift Rule mining Tree called CDR-Tree to solve this problem CDR-Tree can not only find the rule of concept drift also build the prediction model for both old and new data at the same time

无论在大型资料库或现实生活中，同一资料样本的概念有可能会随著时间的递移而改变，也就是产生所谓的概念漂移。当样本发生概念漂移时，由旧有资料所建构的分类模组将不再适用於预测新获得的资料，因此，近年来概念漂移已成为资料探勘中一项热门的研究议题。虽然已有？多学者提出不同的技术来解决概念漂移的问题，但是这些方法都是利用修正或重建的方式来产生适合新资料的预测模组，并无法提供造成概念漂移的原因。然而对使用者而言，其感兴趣的可能正是这些引起概念漂移的规则，如医生可能想了解引起疾病变化的主因、学者会想要知道气候转变的规则、或是决策者想找出顾客购物习惯改变的因素等。因此，本论文提出概念漂移规则探勘树( Concept Drift Rule mining Tree )，简称CDR-Tree，来解决这个问题。CDR-Tree不但能探测出造成概念漂移的主要原因，亦能同时建立新旧资料的预测模组以供决策者运用使用。

The data model is the data source for the integrated mining which is built by using Snow Flake Schema. The integrated mining model consists of local mining and global mining. That is to say, firstly, monitoring cell mining, point mining and monitoring data mining are fulfilled respectively, then global mining model is run to opimize the monitoring process. The paper brings forward how to build up these models and the framework, and what variables should be delivered.

首先，采用雪花结构建立了数据模型构架，为流程挖掘提供数据底层；然后建立了监测流程综合挖掘模型构架，给出了监测单元挖掘、监测点位挖掘和监测数据挖掘，以及综合挖掘的模型函数和参数，以局部优化和全局优化的思路实现了土壤环境监测全流程优化。

But in the course of internationalization, they meet with misunderstanding and puzzlement.

许多企业已经意识到了这一点，但在国际化的进程中，仍存在一些误区与困惑。

Inorder toaccomplish this goal as quickly as possible, we'll beteamingup with anexperienced group of modelers, skinners, and animatorswhosenames willbe announced in the coming weeks.

为了尽快实现这个目标，我们在未来数周内将公布与一些有经验的模型、皮肤、动画制作小组合作。